Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:46:07.771498
Analysis finished2020-08-25 00:46:25.969812
Duration18.2 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.686896085739135e-10
Minimum-2.45656681060791
Maximum2.2068188190460205
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:26.015916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.456566811
5-th percentile-1.604606634
Q1-0.7417185754
median-0.04629806988
Q30.8188895136
95-th percentile1.559158719
Maximum2.206818819
Range4.66338563
Interquartile range (IQR)1.560608089

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)-1495462155
Kurtosis-0.7825889124
Mean-6.686896086e-10
Median Absolute Deviation (MAD)0.7720354199
Skewness-0.04257811404
Sum-3.343448043e-07
Variance1.000000006
2020-08-25T00:46:26.118573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.896482527310.2%
 
0.957877457110.2%
 
-0.981150448310.2%
 
-1.96033799610.2%
 
-0.0088700568310.2%
 
0.093909777710.2%
 
0.18464651710.2%
 
-0.139480739810.2%
 
1.16894626610.2%
 
-0.385896235710.2%
 
0.560857236410.2%
 
-0.728829085810.2%
 
-1.40102064610.2%
 
-1.1336642510.2%
 
0.602856934110.2%
 
-2.04815554610.2%
 
-2.0483117110.2%
 
-1.86262702910.2%
 
0.0145467594310.2%
 
-2.35973358210.2%
 
1.11589455610.2%
 
0.472743958210.2%
 
-0.473311752110.2%
 
-1.3913079510.2%
 
-0.287768483210.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.45656681110.2%
 
-2.35973358210.2%
 
-2.21363520610.2%
 
-2.19645190210.2%
 
-2.0483117110.2%
 
-2.04815554610.2%
 
-2.04329538310.2%
 
-2.0355572710.2%
 
-2.01436543510.2%
 
-1.97440934210.2%
 
ValueCountFrequency (%) 
2.20681881910.2%
 
2.13860535610.2%
 
2.03896999410.2%
 
1.97991383110.2%
 
1.96057844210.2%
 
1.90088903910.2%
 
1.89544475110.2%
 
1.89472794510.2%
 
1.87675476110.2%
 
1.86472463610.2%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2367963790893555e-09
Minimum-1.8134185075759888
Maximum1.673583984375
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:26.231364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.813418508
5-th percentile-1.628256148
Q1-0.8004975766
median0.002659832127
Q30.8870510012
95-th percentile1.54372232
Maximum1.673583984
Range3.487002492
Interquartile range (IQR)1.687548578

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)808540531.7
Kurtosis-1.180988709
Mean1.236796379e-09
Median Absolute Deviation (MAD)0.8641543984
Skewness-0.06915979626
Sum6.183981895e-07
Variance1.000000004
2020-08-25T00:46:26.335559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.393066257210.2%
 
-0.196372151410.2%
 
0.0911030471310.2%
 
-0.591676771610.2%
 
0.345912635310.2%
 
-0.342441439610.2%
 
-0.224443882710.2%
 
0.883129596710.2%
 
0.714162826510.2%
 
0.555983245410.2%
 
1.5541689410.2%
 
1.29751706110.2%
 
-0.519621968310.2%
 
1.5240888610.2%
 
-1.69010949110.2%
 
-1.80112922210.2%
 
-1.34641265910.2%
 
-1.03777742410.2%
 
-1.57439088810.2%
 
-0.110348761110.2%
 
0.870974004310.2%
 
-0.289720654510.2%
 
-1.70575976410.2%
 
-1.7089716210.2%
 
1.58442938310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.81341850810.2%
 
-1.80112922210.2%
 
-1.78299534310.2%
 
-1.77824258810.2%
 
-1.77521574510.2%
 
-1.77276027210.2%
 
-1.77051591910.2%
 
-1.7684352410.2%
 
-1.75655674910.2%
 
-1.75602662610.2%
 
ValueCountFrequency (%) 
1.67358398410.2%
 
1.66840386410.2%
 
1.65017163810.2%
 
1.6447317610.2%
 
1.63882720510.2%
 
1.63864338410.2%
 
1.62952971510.2%
 
1.62579536410.2%
 
1.61995291710.2%
 
1.61869335210.2%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.429610610008239e-10
Minimum-1.7678604125976562
Maximum1.6958022117614746
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:26.455064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.767860413
5-th percentile-1.607560509
Q1-0.8671708703
median0.01831402071
Q30.8621603847
95-th percentile1.559706342
Maximum1.695802212
Range3.463662624
Interquartile range (IQR)1.729331255

Descriptive statistics

Standard deviation0.9999999997
Coefficient of variation (CV)1841752699
Kurtosis-1.16346127
Mean5.42961061e-10
Median Absolute Deviation (MAD)0.8596138954
Skewness-0.03526930882
Sum2.714805305e-07
Variance0.9999999994
2020-08-25T00:46:26.558767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.493649542310.2%
 
1.63738858710.2%
 
0.685661435110.2%
 
-0.286290019810.2%
 
1.09992694910.2%
 
1.05210065810.2%
 
-0.845046937510.2%
 
-0.562968492510.2%
 
-0.646806180510.2%
 
0.574542820510.2%
 
-1.2095791110.2%
 
0.718100905410.2%
 
0.868492782110.2%
 
1.25661182410.2%
 
-0.150962412410.2%
 
-1.53583228610.2%
 
0.939824104310.2%
 
0.16414767510.2%
 
0.812840461710.2%
 
1.52021539210.2%
 
0.264620751110.2%
 
0.900736451110.2%
 
1.09305214910.2%
 
1.32491719710.2%
 
1.33292424710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.76786041310.2%
 
-1.75755715410.2%
 
-1.75153350810.2%
 
-1.73032534110.2%
 
-1.7102304710.2%
 
-1.70823001910.2%
 
-1.70736074410.2%
 
-1.69079518310.2%
 
-1.68975675110.2%
 
-1.68187904410.2%
 
ValueCountFrequency (%) 
1.69580221210.2%
 
1.69189441210.2%
 
1.68885505210.2%
 
1.68532574210.2%
 
1.68297314610.2%
 
1.67766952510.2%
 
1.67646455810.2%
 
1.6703598510.2%
 
1.66858124710.2%
 
1.65875458710.2%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8400605767965316e-09
Minimum-1.773234248161316
Maximum1.6372938156127932
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:26.673940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.773234248
5-th percentile-1.546201175
Q1-0.8796363622
median0.01130548725
Q30.918434009
95-th percentile1.495783859
Maximum1.637293816
Range3.410528064
Interquartile range (IQR)1.798070371

Descriptive statistics

Standard deviation0.999999998
Coefficient of variation (CV)543460367.9
Kurtosis-1.256147053
Mean1.840060577e-09
Median Absolute Deviation (MAD)0.8995948075
Skewness-0.06456493208
Sum9.200302884e-07
Variance0.999999996
2020-08-25T00:46:26.777325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.2422959810.2%
 
1.5397273310.2%
 
0.748358190110.2%
 
1.31382596510.2%
 
0.34488388910.2%
 
-1.00648951510.2%
 
0.690747320710.2%
 
-0.19450019310.2%
 
0.932664871210.2%
 
-1.34280431310.2%
 
-1.55142998710.2%
 
0.847005426910.2%
 
-1.28776335710.2%
 
-0.14998431510.2%
 
0.360705167110.2%
 
0.480148255810.2%
 
0.794563353110.2%
 
-1.51602673510.2%
 
-0.384934812810.2%
 
-1.12568414210.2%
 
1.48701453210.2%
 
0.622260749310.2%
 
0.767331659810.2%
 
0.104241706410.2%
 
0.656598508410.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.77323424810.2%
 
-1.74431085610.2%
 
-1.72943425210.2%
 
-1.72877442810.2%
 
-1.72714924810.2%
 
-1.72116756410.2%
 
-1.71377062810.2%
 
-1.70674824710.2%
 
-1.70571732510.2%
 
-1.69406986210.2%
 
ValueCountFrequency (%) 
1.63729381610.2%
 
1.63378012210.2%
 
1.63132739110.2%
 
1.62840950510.2%
 
1.61423909710.2%
 
1.60697066810.2%
 
1.60351598310.2%
 
1.6021293410.2%
 
1.597377310.2%
 
1.58301091210.2%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.558854460716248e-10
Minimum-1.7141772508621216
Maximum1.6812628507614136
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:26.893077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.714177251
5-th percentile-1.550452679
Q1-0.7951891273
median-0.08507051319
Q30.9066087753
95-th percentile1.515540248
Maximum1.681262851
Range3.395440102
Interquartile range (IQR)1.701797903

Descriptive statistics

Standard deviation0.9999999993
Coefficient of variation (CV)1168380656
Kurtosis-1.241315833
Mean8.558854461e-10
Median Absolute Deviation (MAD)0.891199939
Skewness0.0133117934
Sum4.27942723e-07
Variance0.9999999987
2020-08-25T00:46:26.997767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.49413919410.2%
 
-0.504226982610.2%
 
0.144649416210.2%
 
1.52427315710.2%
 
-0.491851270210.2%
 
-0.74933016310.2%
 
-0.111777812210.2%
 
-0.329744607210.2%
 
0.816601157210.2%
 
1.37366914710.2%
 
1.26194763210.2%
 
1.12953329110.2%
 
1.09793114710.2%
 
1.17514884510.2%
 
1.58461880710.2%
 
-0.172199696310.2%
 
-1.22725927810.2%
 
0.755206346510.2%
 
-1.56705832510.2%
 
-0.243814319410.2%
 
-0.135824486610.2%
 
1.19011402110.2%
 
1.37507355210.2%
 
-1.06962275510.2%
 
0.124225899610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.71417725110.2%
 
-1.71238529710.2%
 
-1.70734524710.2%
 
-1.6913715610.2%
 
-1.68561065210.2%
 
-1.68121790910.2%
 
-1.67621207210.2%
 
-1.66367101710.2%
 
-1.66176974810.2%
 
-1.64436030410.2%
 
ValueCountFrequency (%) 
1.68126285110.2%
 
1.66631174110.2%
 
1.66238784810.2%
 
1.65736401110.2%
 
1.63499605710.2%
 
1.63312411310.2%
 
1.62625837310.2%
 
1.62596774110.2%
 
1.62511205710.2%
 
1.62350082410.2%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.711350917816162e-10
Minimum-1.786902904510498
Maximum1.7054719924926758
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:27.284442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.786902905
5-th percentile-1.618835372
Q1-0.8342062235
median0.00889726961
Q30.8622670919
95-th percentile1.519170809
Maximum1.705471992
Range3.492374897
Interquartile range (IQR)1.696473315

Descriptive statistics

Standard deviation0.9999999969
Coefficient of variation (CV)1296789638
Kurtosis-1.162835465
Mean7.711350918e-10
Median Absolute Deviation (MAD)0.8511648527
Skewness-0.06314205166
Sum3.855675459e-07
Variance0.9999999938
2020-08-25T00:46:27.388329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.366697698810.2%
 
-1.30534625110.2%
 
-0.125321283910.2%
 
0.435701410.2%
 
-0.659632563610.2%
 
-1.05336368110.2%
 
-0.24250997610.2%
 
-1.52602446110.2%
 
0.0236106701210.2%
 
-1.27531611910.2%
 
0.73165690910.2%
 
-1.50007188310.2%
 
1.02213144310.2%
 
-0.554035365610.2%
 
1.03190302810.2%
 
0.00287595577510.2%
 
0.0474472492910.2%
 
-0.300948232410.2%
 
0.592134058510.2%
 
1.26044380710.2%
 
-1.62958836610.2%
 
-0.660498976710.2%
 
-0.25749725110.2%
 
0.287284433810.2%
 
1.0429476510.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.78690290510.2%
 
-1.7797589310.2%
 
-1.77380490310.2%
 
-1.75486111610.2%
 
-1.75444054610.2%
 
-1.75001776210.2%
 
-1.73873317210.2%
 
-1.73405778410.2%
 
-1.72187328310.2%
 
-1.71151232710.2%
 
ValueCountFrequency (%) 
1.70547199210.2%
 
1.69870710410.2%
 
1.67800271510.2%
 
1.67556083210.2%
 
1.66950714610.2%
 
1.66806328310.2%
 
1.66727614410.2%
 
1.66395139710.2%
 
1.66047644610.2%
 
1.63980054910.2%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.527834682768514e-09
Minimum-1.7734127044677734
Maximum1.7361743450164795
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:27.504686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.773412704
5-th percentile-1.563599873
Q1-0.8497746587
median0.06526587531
Q30.8703565598
95-th percentile1.54436751
Maximum1.736174345
Range3.509587049
Interquartile range (IQR)1.720131218

Descriptive statistics

Standard deviation0.9999999984
Coefficient of variation (CV)-654521074.6
Kurtosis-1.168588619
Mean-1.527834683e-09
Median Absolute Deviation (MAD)0.8526991792
Skewness-0.08068761018
Sum-7.639173414e-07
Variance0.9999999968
2020-08-25T00:46:27.610882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.203858107310.2%
 
0.957363545910.2%
 
0.439608722910.2%
 
0.290808111410.2%
 
-0.355137497210.2%
 
-0.737875521210.2%
 
0.675124049210.2%
 
-1.75454688110.2%
 
-0.0325518026910.2%
 
-1.16080737110.2%
 
0.843100607410.2%
 
0.412529647410.2%
 
1.22980964210.2%
 
-1.17448985610.2%
 
-1.62956976910.2%
 
-1.49676120310.2%
 
-1.48901963210.2%
 
0.135337695510.2%
 
1.41278803310.2%
 
0.318836122810.2%
 
-1.04561352710.2%
 
-0.713238000910.2%
 
-0.304861843610.2%
 
0.407539635910.2%
 
1.49289262310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.77341270410.2%
 
-1.76415538810.2%
 
-1.75454688110.2%
 
-1.74814915710.2%
 
-1.74741923810.2%
 
-1.7341458810.2%
 
-1.73402583610.2%
 
-1.73389577910.2%
 
-1.72407996710.2%
 
-1.68193733710.2%
 
ValueCountFrequency (%) 
1.73617434510.2%
 
1.73048675110.2%
 
1.72521460110.2%
 
1.71259295910.2%
 
1.71243214610.2%
 
1.71143221910.2%
 
1.70650315310.2%
 
1.69889402410.2%
 
1.69298875310.2%
 
1.67577290510.2%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.33996319770813e-10
Minimum-1.7134811878204346
Maximum1.7497831583023071
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:27.726465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.713481188
5-th percentile-1.555032939
Q1-0.8179045469
median-0.02204973809
Q30.9052096009
95-th percentile1.535334349
Maximum1.749783158
Range3.463264346
Interquartile range (IQR)1.723114148

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-2304167004
Kurtosis-1.221936926
Mean-4.339963198e-10
Median Absolute Deviation (MAD)0.8454941511
Skewness0.01219819349
Sum-2.169981599e-07
Variance1
2020-08-25T00:46:27.834058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.52538764510.2%
 
0.348783761310.2%
 
-1.18403160610.2%
 
1.0864802610.2%
 
-0.00796944554910.2%
 
-1.09431195310.2%
 
-0.0207607243210.2%
 
-0.197580426910.2%
 
-1.42521226410.2%
 
0.0417050495710.2%
 
-1.07089245310.2%
 
-1.71348118810.2%
 
0.42055869110.2%
 
-0.316230028910.2%
 
-0.741510689310.2%
 
-0.938778936910.2%
 
1.37565779710.2%
 
-0.474457621610.2%
 
-0.605544030710.2%
 
-0.396153479810.2%
 
1.03382730510.2%
 
1.06117415410.2%
 
-1.3685096510.2%
 
0.624339461310.2%
 
-0.421489328110.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.71348118810.2%
 
-1.70940458810.2%
 
-1.68090474610.2%
 
-1.6771396410.2%
 
-1.66819620110.2%
 
-1.66557085510.2%
 
-1.65868425410.2%
 
-1.65803039110.2%
 
-1.64546310910.2%
 
-1.63850450510.2%
 
ValueCountFrequency (%) 
1.74978315810.2%
 
1.73642671110.2%
 
1.73513805910.2%
 
1.70852804210.2%
 
1.70113253610.2%
 
1.7004580510.2%
 
1.68172407210.2%
 
1.67547154410.2%
 
1.66906273410.2%
 
1.66131305710.2%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.526903361082077e-09
Minimum-1.7501331567764282
Maximum1.7165577411651611
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:27.947696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.750133157
5-th percentile-1.533810687
Q1-0.8681935072
median-0.05908546224
Q30.8895753473
95-th percentile1.548455989
Maximum1.716557741
Range3.466690898
Interquartile range (IQR)1.757768854

Descriptive statistics

Standard deviation0.999999997
Coefficient of variation (CV)654920293.2
Kurtosis-1.229275533
Mean1.526903361e-09
Median Absolute Deviation (MAD)0.8812505305
Skewness0.03856401021
Sum7.634516805e-07
Variance0.9999999939
2020-08-25T00:46:28.050322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.33397781810.2%
 
0.920242428810.2%
 
-1.736916910.2%
 
-0.116784438510.2%
 
1.67247450410.2%
 
1.62463879610.2%
 
-1.20362019510.2%
 
-0.868466913710.2%
 
-0.179031610510.2%
 
-0.0494192391610.2%
 
-1.35217964610.2%
 
0.985661745110.2%
 
1.52797484410.2%
 
-0.44962701210.2%
 
0.882153570710.2%
 
-0.636063814210.2%
 
-0.959608018410.2%
 
1.56119501610.2%
 
-0.147935524610.2%
 
0.366049975210.2%
 
-0.431066542910.2%
 
-0.954440116910.2%
 
-1.00212311710.2%
 
0.105267621610.2%
 
1.5377975710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.75013315710.2%
 
-1.736916910.2%
 
-1.72902309910.2%
 
-1.72815275210.2%
 
-1.71868395810.2%
 
-1.7096213110.2%
 
-1.68757045310.2%
 
-1.6785221110.2%
 
-1.6777261510.2%
 
-1.67477512410.2%
 
ValueCountFrequency (%) 
1.71655774110.2%
 
1.68553066310.2%
 
1.68489575410.2%
 
1.68400478410.2%
 
1.68378341210.2%
 
1.67829835410.2%
 
1.67247450410.2%
 
1.64135360710.2%
 
1.62576067410.2%
 
1.62463879610.2%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2349337339401246e-09
Minimum-1.7841724157333374
Maximum1.8537031412124636
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:28.161478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.784172416
5-th percentile-1.600159085
Q1-0.7962742597
median0.01182358852
Q30.8080052882
95-th percentile1.600343585
Maximum1.853703141
Range3.637875557
Interquartile range (IQR)1.604279548

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-809760049.9
Kurtosis-1.080843031
Mean-1.234933734e-09
Median Absolute Deviation (MAD)0.8019378483
Skewness0.004368841481
Sum-6.17466867e-07
Variance1.000000004
2020-08-25T00:46:28.273831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.857421696210.2%
 
-0.179281264510.2%
 
-0.611629784110.2%
 
-0.865537524210.2%
 
-0.677064597610.2%
 
0.0502569824510.2%
 
-0.703435540210.2%
 
-0.766918182410.2%
 
-1.01821649110.2%
 
0.671278595910.2%
 
0.482225477710.2%
 
0.746184468310.2%
 
-0.0578149706110.2%
 
1.59193563510.2%
 
-0.394694954210.2%
 
0.582360386810.2%
 
0.411667168110.2%
 
-0.361005067810.2%
 
-1.78417241610.2%
 
0.0915942266610.2%
 
-0.904498636710.2%
 
1.22919261510.2%
 
0.100350126610.2%
 
-0.314625024810.2%
 
-0.247889414410.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.78417241610.2%
 
-1.77739059910.2%
 
-1.77502846710.2%
 
-1.77189278610.2%
 
-1.76929485810.2%
 
-1.76071715410.2%
 
-1.75078654310.2%
 
-1.74618196510.2%
 
-1.73531246210.2%
 
-1.71016371310.2%
 
ValueCountFrequency (%) 
1.85370314110.2%
 
1.84002113310.2%
 
1.83738994610.2%
 
1.83321058810.2%
 
1.82763361910.2%
 
1.80349266510.2%
 
1.79186856710.2%
 
1.77559721510.2%
 
1.74801683410.2%
 
1.74639868710.2%
 

target
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.685754776000976e-11
Minimum-2.48649263381958
Maximum2.338509082794189
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:46:28.392112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.486492634
5-th percentile-1.725865155
Q1-0.7074823678
median0.1010490581
Q30.7800683975
95-th percentile1.450444555
Maximum2.338509083
Range4.825001717
Interquartile range (IQR)1.487550765

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-1.032444063e+10
Kurtosis-0.6739426583
Mean-9.685754776e-11
Median Absolute Deviation (MAD)0.7286494747
Skewness-0.2698121415
Sum-4.842877388e-08
Variance1.000000003
2020-08-25T00:46:28.489838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.7421935810.2%
 
-0.458178073210.2%
 
0.811541199710.2%
 
0.672191679510.2%
 
0.638519823610.2%
 
-0.269487410810.2%
 
-0.064616039410.2%
 
0.484050065310.2%
 
0.452313393410.2%
 
0.487958341810.2%
 
2.33850908310.2%
 
1.61199605510.2%
 
0.670256853110.2%
 
1.88934624210.2%
 
-0.228545293210.2%
 
1.79228115110.2%
 
1.28395092510.2%
 
0.491383165110.2%
 
0.0701115876410.2%
 
-0.84898054610.2%
 
-0.0190234165610.2%
 
0.107221558710.2%
 
1.20187556710.2%
 
0.803087711310.2%
 
-0.694690048710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.48649263410.2%
 
-2.37213563910.2%
 
-2.27773785610.2%
 
-2.17237281810.2%
 
-2.12826442710.2%
 
-2.11221671110.2%
 
-2.10801148410.2%
 
-2.06490731210.2%
 
-2.02516722710.2%
 
-1.99152696110.2%
 
ValueCountFrequency (%) 
2.33850908310.2%
 
2.20383143410.2%
 
2.16491150910.2%
 
1.88934624210.2%
 
1.88365924410.2%
 
1.85633695110.2%
 
1.84807324410.2%
 
1.83829784410.2%
 
1.83059585110.2%
 
1.79568290710.2%
 

Interactions

2020-08-25T00:46:08.239841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:08.357620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:08.485481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:08.614110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:08.744986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:08.874998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.008266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.135513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.275058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.402473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.533153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.653614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.782478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:09.920168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:10.062273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:10.202421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:10.341956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:10.486853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:10.624863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:10.926312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.063787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.201963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.332027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.466295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.607313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.746340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:11.884807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.026958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.166843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.304592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.440920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.582071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.723448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.853237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:12.982950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.123119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.261381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.398207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.539276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.679209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.823753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:13.963372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:14.105041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:14.247750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:14.379143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:14.509339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:14.652121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:14.793000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.104863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.239772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.380556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.518277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.658477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.797768image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:15.944799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.080718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.213541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.354193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.492926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.634931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.774794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:16.914738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.055088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.194783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.335714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.477541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.611733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.743473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:17.883949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.023164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.163989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.304304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.442512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.582359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.735821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:18.874409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:19.015314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:19.316239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:19.448346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:19.584171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:19.724577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:19.868751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.011623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.151986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.288234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.427101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.564810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.705193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.834319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:20.964345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.102490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.243661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.384239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.521471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.661396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.799421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:21.938064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.076716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.220165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.350646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.485670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.627538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.772441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:22.914052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:23.055910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:23.200577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:23.521981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:23.666266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:23.811940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:23.956567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.093251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.214910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.341954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.470479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.598630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.727596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.854881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:24.982639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:25.111881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:25.240449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:25.371590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:46:28.608208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:46:28.835779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:46:29.055279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:46:29.278080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:46:25.602307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:46:25.870324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
0-0.131331-0.7402711.470037-1.0064900.3826821.5107020.135142-0.983733-1.568228-1.0978520.874839
1-1.568566-1.417031-0.866602-0.440028-1.157621-0.462024-0.3050861.2924561.244658-1.161871-0.514424
2-1.214875-1.7315080.566648-1.447824-0.787260-1.224079-1.3363190.0156900.543585-0.474572-1.096513
30.7586901.363859-0.8450470.443047-1.1284380.7916440.558782-0.317406-1.3321040.458430-1.176170
4-0.599134-0.7811290.0790981.174417-0.3826711.6192560.7817150.285455-0.6319781.2042041.120685
5-0.314664-1.0542781.1720170.318237-0.5042270.366189-0.821731-1.6681960.4406721.2962620.914841
60.9008811.152829-0.503426-0.6332481.350291-1.697089-1.477959-0.3193380.783438-1.533983-1.234993
7-0.412137-0.7446250.864334-0.5996020.999694-0.317854-1.485136-0.9795221.563771-0.7876600.700320
80.839314-0.0216191.629632-0.461601-1.265026-1.0126660.9149660.6434700.700998-0.015353-0.695228
90.119334-0.9383211.677670-0.260191-1.2952960.7625231.262591-0.8324130.9696711.5395450.973490

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
4900.4590830.708778-0.4349071.465988-0.448096-1.2204480.3066781.5005121.4576121.227921-1.329584
4911.0598381.0058210.861436-0.9574301.681263-1.1261901.4077120.3971710.7417051.414316-1.420658
4921.5175751.0807591.186693-0.969985-0.4789150.5921341.698894-0.448394-0.1479360.491703-0.272746
4931.0594660.8744830.685661-0.050934-1.615082-0.0421231.6519270.822617-0.532240-0.143609-1.660092
494-1.040173-1.7829950.7451791.5427551.3750740.5782741.2439450.5983551.621365-0.8080690.313461
4950.9529730.8869620.115963-0.242129-1.4692641.298301-0.0133081.2573000.0207621.169321-2.025167
4960.5777170.6955340.489078-0.4031350.934241-0.964520-0.380957-1.621349-1.345499-0.563555-1.554008
497-1.404767-0.938658-1.6498970.323531-1.6856111.4437810.3188361.630809-0.746579-0.1620170.551016
498-0.151633-0.2056640.9542500.3344380.831764-1.289162-0.697444-0.3224510.494507-0.2200641.178861
4990.9932500.8001871.301865-1.5514300.477188-0.185062-0.7027931.669063-0.6452600.938805-1.900633